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Recover Dataproc jobs after worker crashes - #72417

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1fanwang:recover-dataproc-jobs
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1fanwang:recover-dataproc-jobs

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@1fanwang 1fanwang commented Sep 2, 2026 •

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A synchronous Dataproc job can outlive its worker, but a task retry currently submits a second job instead of reconnecting. After this change, the retry restores the API-returned job ID from task state and resumes waiting for the same job.

DataprocSubmitJobOperator uses the AIP-103 resumable-job contract only on its synchronous wait path. Active jobs reconnect, completed jobs return without another submission, and terminal or missing jobs submit fresh. Recovery restores the operator job ID and extra link state used by cancellation. Asynchronous, deferrable, direct-to-trigger, OpenLineage, and pre-Airflow 3.3 behavior stay unchanged.

An explicit request_id is still passed unchanged. Dataproc may therefore return the prior terminal job when a retry resubmits with the same idempotency key.

Testing

$ PATH="$PWD/.venv/bin:$PATH" AIRFLOW_HOME="$PWD/.build/airflow-home-dataproc-final" uv run --project providers/google pytest providers/google/tests/unit/google/cloud/operators/test_dataproc.py -q
125 passed, 1 warning in 97.29s

A scratch harness executed two operator instances against a local Dataproc hook and the real TaskStateStoreAccessor path:

Commands and raw crash/retry output
$ AIRFLOW_HOME="$PWD/.build/airflow-home-baseline-approved" PYTHONPATH="$PWD/.build/dataproc-baseline/airflow-core/src:$PWD/.build/dataproc-baseline/task-sdk/src:$PWD/.build/dataproc-baseline/providers/google/src" PATH="$PWD/.venv/bin:$PATH" uv run --project providers/google python dev/dataproc_resumability_e2e.py --label baseline | tee .build/dataproc-proof-baseline-approved.log
{
  "first_attempt_error": "WorkerCrash: simulated worker crash while waiting for Dataproc job",
  "label": "baseline",
  "link_job_ids": [
    "dataproc-job-1",
    "dataproc-job-2"
  ],
  "recovered_without_resubmission": false,
  "second_operator_job_id": "dataproc-job-2",
  "second_result": "dataproc-job-2",
  "status_reads": [],
  "stored_state_after_crash": {},
  "submissions": [
    "dataproc-job-1",
    "dataproc-job-2"
  ],
  "waited_job_ids": [
    "dataproc-job-1",
    "dataproc-job-2"
  ]
}
$ AIRFLOW_HOME="$PWD/.build/airflow-home-patched-approved" PYTHONPATH="$PWD/airflow-core/src:$PWD/task-sdk/src:$PWD/providers/google/src" PATH="$PWD/.venv/bin:$PATH" uv run --project providers/google python dev/dataproc_resumability_e2e.py --label patched | tee .build/dataproc-proof-patched-approved.log
{
  "first_attempt_error": "WorkerCrash: simulated worker crash while waiting for Dataproc job",
  "label": "patched",
  "link_job_ids": [
    "dataproc-job-1",
    "dataproc-job-1"
  ],
  "recovered_without_resubmission": true,
  "second_operator_job_id": "dataproc-job-1",
  "second_result": "dataproc-job-1",
  "status_reads": [
    "dataproc-job-1"
  ],
  "stored_state_after_crash": {
    "dataproc_job_id": "dataproc-job-1"
  },
  "submissions": [
    "dataproc-job-1"
  ],
  "waited_job_ids": [
    "dataproc-job-1",
    "dataproc-job-1"
  ]
}

Was generative AI tooling used to co-author this PR?
  • Yes - GitHub Copilot CLI (GPT-5.6 Sol)

Generated-by: GitHub Copilot CLI (GPT-5.6 Sol) following the guidelines


Synchronous Dataproc tasks can submit duplicate jobs when a worker exits after submission but before task completion.

Generated-by: GitHub Copilot CLI (GPT-5.6 Sol)
Signed-off-by: 1fanwang <[email protected]>
@boring-cyborg boring-cyborg Bot added area:providers provider:google Google (including GCP) related issues labels Sep 2, 2026
@potiuk potiuk added the closed because of open PR limit Closed as a one-time step of introducing the open pull request limit label Sep 25, 2026
@potiuk

potiuk commented Sep 25, 2026

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Hello @1fanwang - thank you for your contributions to Apache Airflow!

The Airflow community has introduced a limit of 5 open pull requests at a time for contributors without write access to the repository. You currently have 34 open pull requests, so - as a one-time step of introducing the limit - we closed the ones where maintainers have not engaged yet:

These pull requests stay open because maintainers are already engaged in them - they count towards your limit:

This is not a judgement of you or of your changes. We never told contributors before that opening many pull requests at once was a problem, so there is nothing to feel bad about - and nothing is lost: your branches, commits and the review history stay where they are.

What we ask you to do is to make your first prioritization decision: choose which of the pull requests above matter most to you, and reopen them (up to 5 open at a time, including the ones still open) with the "Reopen pull request" button or gh pr reopen <PR_NUMBER> --repo apache/airflow. Reopen the ones you are ready to follow through - keep them rebased, respond to review comments and fix failing checks.

While your pull requests are waiting for review, the most valuable thing you can do is help in other ways - reviewing other contributors' pull requests, helping with issues, and taking part in the discussions on the devlist and Slack.

Why we introduced the limit, what it means for you and how to reopen or restore a pull request is explained in https://github.com/apache/airflow/blob/main/contributing-docs/32_open_pull_request_limit.rst.


Drafted-by: Claude Code (Opus 5); reviewed by @potiuk before posting

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